Traffic hindrance risk prediction apparatus

ABSTRACT

An apparatus for predicting a traffic hindrance risk having a traffic hindrance information generating server configured to generate traffic hindrance information encompassing congestion degree information including a current congestion degree obtained from a current value of driving data transmitted as probe data regarding a driving route from a vehicle equipped with a navigation system and a statistical congestion degree obtained from a statistical value of the current value of driving data in a certain previous period and a weather information generating server configured to generate weather information from weather data distributed by a meteorological agency with respect to an area including the driving route based on an weather model. In the apparatus, a traffic hindrance occurrence risk possibility of traffic hindrance occurrence with respect to the driving route is predicted from the congestion degree and weather information.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2016-187707 filed on Sep. 27, 2016, thecontents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates to a traffic hindrance risk prediction apparatus,particularly to an apparatus that predicts traffic hindrance risk basedon traffic hindrance information and weather information generated by atraffic hindrance information generating server and a weatherinformation generating server.

Description of Related Art

As an example of an apparatus of this type can be cited that describedin Japanese Patent No. 4,058,046. The technology set out in thereference is configured to predict snow drift amount from the measuredheight of snow bank accumulated when snow is cleared away along arailroad line and weather forecast data. It has a database that storessnow drift amount from measured data and predicts a snowfall forecastvalue as a predicted snow drift amount when no snowfall is forecastedfrom the weather forecast data. On the other hand, when snowfall isforecasted, a corresponding snow drift analysis model is retrieved fromthe measured data and weather forecast data, and is outputtedperiodically as the predicted snow drift amount.

The technology of the reference is configured to predict a traffichindrance risk of the snowdrift amount based on weather informationgenerated by weather forecast data but does not go beyond this, andsince it is not constituted to have a traffic hindrance risks predictingunit that predicts traffic hindrance occurrence risk from trafficinformation including congestion degree acquired from driving datatransmitted from vehicles and weather information, it disadvantageouslydoes not predict traffic hindrance risk that could possibly occur infuture.

SUMMARY OF THE INVENTION

Therefore, an object of this invention is to overcome the aforesaidproblem by providing a traffic hindrance risk prediction apparatusconfigured to predict possible traffic hindrance occurrence risks basedon traffic hindrance information meaning congestion degree acquired fromdriving data transmitted from vehicles and weather information.

In order to achieve the object, this invention provides in its firstaspect an apparatus for predicting a traffic hindrance risk, comprising:a traffic hindrance information generating server configured to generatetraffic hindrance information encompassing congestion degree informationincluding a current congestion degree obtained from a current value ofdriving data transmitted as probe data on a driving route from a vehicleequipped with a navigation system and a statistical congestion degreeobtained from a statistical value of the current value of driving datain a certain previous period; a weather information generating serverconfigured to generate weather information from weather data distributedby a meteorological agency with respect to an area including the drivingroute based on an weather model; and a traffic hindrance risk predictingunit configured to predict a traffic hindrance occurrence riskpossibility of traffic hindrance occurrence on the driving route basedon the congestion degree information generated by the traffic hindranceinformation generating server and the weather information generated bythe weather information generating server.

In order to achieve the object, this invention provides in its secondaspect an apparatus for predicting a traffic hindrance risk, comprising:a traffic hindrance information generating server configured to generatetraffic hindrance information encompassing congestion degree informationincluding a current congestion degree obtained from a current value ofdriving data transmitted as probe data on a driving route from a vehicleequipped with a navigation system and a statistical congestion degreeobtained from a statistical value of the current value of driving datain a certain previous period; a weather information generating serverconfigured to generate weather information from weather data distributedby a meteorological agency with respect to an area including the drivingroute based on an weather model; and at least one processor and a memorycoupled to the processor; wherein the processor and the memory areconfigured to perform; predicting a traffic hindrance occurrence riskpossibility of traffic hindrance occurrence on the driving route basedon the congestion degree information generated by the traffic hindranceinformation generating server and the weather information generated bythe weather information generating server.

In order to achieve the object, this invention provides in its thirdaspect a method for predicting a traffic hindrance risk, having: atraffic hindrance information generating server configured to generatetraffic hindrance information encompassing congestion degree informationincluding a current congestion degree obtained from a current value ofdriving data transmitted as probe data on a driving route from a vehicleequipped with a navigation system and a statistical congestion degreeobtained from a statistical value of the current value of driving datain a certain previous period; and a weather information generatingserver configured to generate weather information from weather datadistributed by a meteorological agency with respect to an area includingthe driving route based on an weather model; wherein the methodcomprising the step of; predicting a traffic hindrance occurrence riskpossibility of traffic hindrance occurrence on the driving route basedon the congestion degree information generated by the traffic hindranceinformation generating server and the weather information generated bythe weather information generating server.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects, features, and advantages of the present invention willbecome clearer from the following description of embodiments in relationto the attached drawings, in which:

FIG. 1 is a schematic diagram generally illustrating a traffic hindrancerisk prediction apparatus in accordance with an embodiment of thisinvention;

FIG. 2 is a flowchart showing operations (processing) of a traffichindrance information generating server shown in FIG. 1;

FIG. 3 is a similar flowchart showing operations (processing) of thetraffic hindrance information generating server shown in FIG. 1;

FIG. 4 is an explanatory diagram showing congestion degree informationgenerated by the traffic hindrance information generating server of FIG.1 and sent to the traffic hindrance risk prediction apparatus;

FIG. 5 is an explanatory diagram showing strand risk, traffic hindrancerisk, and possibility of traffic hindrance risk of FIG. 4 in greaterdetail;

FIG. 6 is an explanatory diagram for explaining operations (processing)of the traffic hindrance risk prediction apparatus of FIG. 1 forpredicting traffic hindrance occurrence risk defining possibility oftraffic hindrance occurring during a predetermined future period;

FIG. 7 is a flowchart for explaining operations (processing) shown inFIG. 6 of the traffic hindrance risk prediction apparatus shown in FIG.1;

FIG. 8 is a flowchart for explaining, inter alia, mesh surfaceconversion processing (operations) of the traffic hindrance riskindicating apparatus of FIG. 1;

FIG. 9 is an explanatory diagram for explaining processing of theflowchart of FIG. 8;

FIG. 10 is likewise a flowchart for explaining processing of theflowchart of FIG. 8;

FIG. 11 is likewise a flowchart for explaining processing of theflowchart of FIG. 8;

FIG. 12 is likewise a flowchart for explaining processing of theflowchart of FIG. 8;

FIG. 13 is likewise a flowchart for explaining processing of theflowchart of FIG. 8; and

FIG. 14 is a flowchart for explaining, inter alia, other mesh surfaceconversion processing (operations) of the traffic hindrance riskprediction apparatus of FIG. 1.

DETAILED DESCRIPTION OF THE INVENTION

An embodiment of the traffic hindrance risk prediction apparatusaccording to this invention is explained with reference to the attacheddrawings in the following.

FIG. 1 is a schematic diagram generally illustrating a traffic hindrancerisk prediction apparatus in accordance with an embodiment thisinvention.

Now to explain, symbol 10 designates the traffic hindrance riskprediction apparatus (hereinafter called “apparatus 10”). The apparatus10 comprises (has) one or more processors (CPUs) each equipped with aCPU, ROM, RAM, I/O and the like, and is connected to a traffic hindranceinformation generating server 12 and a weather information generatingserver 14.

The traffic hindrance information generating server 12 is constituted bya large-scale computer installed at a predetermined location (e.g., afacility owned by the present applicant).

The weather information generating server 14 is also similarlyconstituted by a large-scale computer installed at a predeterminedlocation (e.g., a facility owned by the present applicant).

A communication-type navigation system (hereinafter called “navisystem”) 16 a is installed in a vehicle(s) (i.e., floating car(s)) 16.When driving on a road, each of the vehicles 16 transmits probe data(i.e., floating car data; fcd or driving data) 20 through acommunication module 16 a 1 and a probe collection module 16 a 2 builtinto the navi system 16 a. The probe collection module 16 a 2 isequipped with a GPS unit 18 that receives position determination datafrom multiple GPS satellites and detects position of the vehicle 16.

The navi system 16 a is constituted as a telematics-based drive supportsystem operated as a membership system by the present applicant underthe names Internavi and Internavi System (both registered trademarks).

Based on probe data 20 from drivers (members of the system) of a largenumbers of system-equipped vehicles (only one vehicle shown) 16, drivingconditions of the driving route are detected in the navi system 16 a andthe detected driving conditions are distributed on request to themember-driven vehicles 16 including the one concerned.

The traffic hindrance information generating server 12 receives drivingdata transmitted from vehicles 16 driving on the driving route as probedata 20, i.e., as driving data transmitted from the vehicles 16 on thedriving route. By driving data is meant transmit time data, transmitposition data, and the like.

The traffic hindrance information generating server 12 comprises a link(mapped data) DB (database) 12 a, a traffic information generating unit12 b, a current congestion degree calculating unit 12 c, a statisticalcongestion degree calculating unit 12 d, a traffic hindrance riskcalculating unit 12 e, and an API (Application Program Interface) 12 f

The weather information generating server 14 receives weather data(including weather observation values, weather analysis values, andweather forecast values) 14 b distributed by a meteorological agency (oragency related thereto) 14 a. The weather information generating server14 comprises a mesh unit calculating unit 14 c, a weather model DB(database) 14 d, a rain DB (database) 14 e, a snow DB (database) 14 f, afoul-weather traffic hindrance risk table generating unit 14 g, and anAPI 14 h.

As is well known, a “mesh” is a unit into which a region is dividedlattice-like (e.g., primary mesh of approx. 80 km cell size, secondarymesh of approx. 10 km cell size, and tertiary mesh of 1 km cell size)and derivatively means data of various information within that range.

The processing performed by the mesh unit calculating unit 14 c and thelike enables the weather information generating server 14 to utilize theweather model stored in the weather model DB 14 d to generate weatherinformation from the weather data 14 b distributed by the meteorologicalagency 14 a with respect to an area including the driving route.

The apparatus 10 is connected to the traffic hindrance informationgenerating server 12 and the weather information generating server 14through a mobile telephone communication network, a public telephonecommunication network and other communication networks 22. The apparatus10 comprises a traffic hindrance risk calculating unit 10 a configuredto calculate traffic hindrance risk in link units, a traffic hindrancerisk predicting unit 10 b configured to predict traffic hindrance riskbased on calculated traffic hindrance risk, a map/indication tablegenerating unit 10 c, a first traffic hindrance risk indication datagenerating unit 10 d configured to generate link-unit indication data ofcalculated traffic hindrance risks, a mesh surface conversion processingunit 10 e configured to perform conversion-processing on a surface of amesh map of traffic hindrance risks calculated by link units, a secondtraffic hindrance risk indication data generating unit 10 f configuredto generate indication data of traffic hindrance risk calculated insurface units, and a display 10 g configured to display and indicatethese data.

In the apparatus 10, the traffic hindrance risk predicting unit 10 bfunction as a traffic hindrance risk predicting unit and predictstraffic hindrance risk that means a possibility that traffic hindrancecould occur on the driving route based on traffic hindrance informationgenerated by the traffic hindrance information generating server 12 andweather information generated by the weather information generatingserver 14.

It should be noted that, although not shown, the apparatus 10 isconfigured such that the traffic hindrance risk predicting unit canaccess the indication data generated by the first and second traffichindrance risk indication data generating units 10 d, 10 f and indicatedby the display 10 g.

The apparatus 10 additionally comprises a GPS unit 24 that receivesposition determination data from multiple GPS satellites and detectsposition of the apparatus 10 itself and a switch 30 connected to a powersupply 26, and the traffic hindrance risk predicting unit 10 b isoperated by an ON operation of the switch 30 by an operator (user) ofthe apparatus 10. In other words, the apparatus 10 is constituted as anindependent apparatus complete in itself.

Therefore, an operator with the apparatus 10 on his or her person candrive the vehicle(s) 16 or other four-wheeled vehicle(s), amotorcycle(s) or a bicycle(s), or can walk while carrying the apparatus10. Moreover, the apparatus 10 can be built into the navi system 16 a orbe built into a smartphone or other mobile terminal 32. When theapparatus 10 is built into a mobile terminal 32, it also becomespossible for an operator to drive the vehicle(s) 16 or the like with themobile terminal 32 on his person.

When the apparatus 10 is built into the navi system 16 a or the mobileterminal 32, analogous equipment of the navi system 16 a or mobileterminal 32 serve in place of the GPS unit 24, power supply 26, switch30, display 10 g, and so on.

For convenience of explanation in the following, a case of the apparatus10 being built into the navi system 16 a is explained as an example.

Now follows a detailed explanation of operation of the traffic hindranceinformation generating server 12 and apparatus 10 with reference to,inter alia, the flowchart of FIG. 2.

FIG. 2 is a flowchart explaining operations (processing) of the trafficinformation generating unit 12 b, current congestion degree calculatingunit 12 c, statistical congestion degree calculating unit 12 d, etc. ofthe traffic hindrance information generating server 12.

Now to explain, probe measurement is performed first (S10 (S: processingStep)). Specifically, the traffic information generating unit 12 breceives driving data transmitted from the vehicle(s) 16 regarding thedriving route on which the vehicle(s) 16 is currently positioned asprobe data 20, affixes a data generation date/time (date and timeuploaded as probe data 20), and sends data to the current congestiondegree calculating unit 12 c together with the link corresponding to thedriving route concerned stored in the link DB 12 a. (S12).

The driving route (road) is represented by a series of links. Each linkamounts to a vector connecting adjacent nodes (intersections, forks, orother points where multiple roads meet).

The current congestion degree calculating unit 12 c detects thegeneration date/time of the received driving data and deems the drivingdata to be current values when determined to be within less than 30 minfrom current (present) time, compares them with multiple suitably setthreshold values, and classifies the current values on the linkconcerned of the vehicle(s) 16 as smooth, heavy or jammed (calculatescurrent congestion degree).

Further, when generation date/time of received driving data isdetermined to be 30 min or more from current time, the currentcongestion degree calculating unit 12 c deems the driving data to bestatistical values (aggregate values) and sends the driving data to thestatistical congestion degree calculating unit 12 d (S14).

The statistical congestion degree calculating unit 12 d calculatedriving time and average driving speed from start to end of the linkconcerned uses the received driving data to and compares the calculatedaverage driving speed with multiple appropriately defined thresholdvalues. Based on the comparison results and number of vehicle(s) 16 perunit time, the statistical congestion degree calculating unit 12 dclassifies average driving speed of vehicle(s) 16 on the link concernedas smooth, heavy or jammed (calculates statistical congestion degree).Statistical congestion degrees are aggregated by day of week and time ofday. Thus the traffic information includes data generation time/date andcongestion degree.

FIG. 3 is a flowchart explaining operations of the traffic hindrancerisk calculating unit 12 e of the traffic hindrance informationgenerating server 12.

Now to explain, first, in S100, a first link among links for whichhindrance information is to be generated is retrieved. Actually, thetraffic hindrance information generating server 12 generates congestiondegree information comprising current congestion degree and statisticalcongestion degree for all of predetermined driving sections (e.g., 10 kmsections) individually and starts by selecting the first (start side)link among the links constituting the driving sections.

Next, in S102, it is determined whether a current congestion degreealready exists, namely whether one has been calculated.

When the result in S102 is NO, the program returns to S100 and the nextlink is selected, and when YES, goes to 5104, in which congestion degreeinformation comprising current congestion degree and statisticalcongestion degree is generated and indicated at an appropriate memoryregion.

Next, in S106, it is determined whether any un-generated link exists,and when the result is YES, the program repeats the aforesaidprocessing, and when NO, discontinues further processing. The reason fordetermining presence/absence of current congestion degree in S102 isthat while statistical congestion degree is a value for a certain pastperiod and naturally exists, there are cases in which no currentcongestion degree has yet been calculated.

Returning to explanation of FIG. 1, congestion degree informationgenerated by the traffic hindrance information generating server 12 andweather information generated by the weather information generatingserver 14 by applying the weather data 14 b distributed by themeteorological agency 14 a with respect to an area including the drivingroute concerned to the weather model stored in the weather model DB 14 dare sent through the API 12 f and API 14 h to the apparatus 10.

The sent information is sorted by link units in the traffic hindrancerisk calculating unit 10 a, thereafter sent from the traffic hindrancerisk calculating unit 10 a to the traffic hindrance risk predicting unit10 b, the map/indication table generating unit 10 c and so on, and alsosent to the mesh surface conversion processing unit 10 e.

Congestion degree information generated by the traffic hindranceinformation generating server 12 and sent to the traffic hindrance riskcalculating unit 10 a is shown at the top of FIG. 4.

As shown, a “statistical congestion degree” is a past value (usualvalue) during a given time period, that is classified into one of threebroad categories (multiple level categories): smooth, heavy and jammed.In contrast, a “current congestion degree” is a recent value (withinless than past 30 min), and since it varies with driving time of day andother factors, it is differentiated (classified) into one of threecategories, smooth, heavy and jammed, even in a smooth category ofusually sparse traffic.

In their relation to the statistical congestion degrees, currentcongestion degrees are no different between the heavy traffic categoryin which traffic is usually heavy and the jammed category in whichtraffic is usually jammed. Current congestion degrees are thereforesubdivided into nine categories relative to the three statisticalcongestion degree categories.

“Congestion degree information” is a generic term encompassing currentcongestion degree and statistical congestion degree, but concretelyspeaking congestion degree information comprising numerical values 1 to9 defined regarding the current congestion degree categories within thethree statistical congestion degree categories. As indicated, 1 to 3mean current congestion degree of “jammed” within the associatedstatistical congestion degrees; namely, the value is defined as 1 whenstatistical congestion degree is jammed, as 2 when statisticalcongestion degree is heavy, and as 3 when statistical congestion degreeis smooth.

Further, 4 to 6 mean current congestion degree of “heavy” within theassociated statistical congestion degrees; namely, the value is definedas 4 when statistical congestion degree is jammed, as 5 when statisticalcongestion degree is heavy, and as 6 when statistical congestion degreeis smooth.

Further, 7 to 9 mean current congestion degree of “smooth” within theassociated statistical congestion degrees; namely, the value is definedas 7 when statistical congestion degree is jammed, as 8 when statisticalcongestion degree is heavy, and as 9 when statistical congestion degreeis smooth.

Thus, congestion degree information sorted into multiple levels,specifically current congestion degree sorted into nine categories withrespect to statistical congestion degree sorted into three categories,is in the foregoing manner sorted (classified) into multiple (nine)level categories of congestion degree information and assigned numericalvalues 1 to 9 that decrease with increasing congestion degree.

Moreover, the numerical values (congestion degree information) areindicated in the display 10 g by colors defined to differ accordingly.Specifically, values 1 to 3 are colored red, values 4 to 6 orange, andvalues 7 to 9 green.

In addition, the colors are defined to vary in shade so as to becomedarker as degree of traffic hindrance (congestion degree) or extent ofweather condition hindrance increases. Moreover, red, orange and greencoloring is also applied to the driving route (links) in accordance withtraffic hindrance (congestion degree) or extent of weather conditionhindrance.

For convenience of illustration in the drawings, red coloring isindicated by dashed lines, orange coloring by one-dot-dashed lines andgreen coloring by two-dot-dashed lines, and shading of these colors isindicated by interval size of hatching lines of the line typescorresponding to the colors (namely, darker color shading is indicatedby narrower hatching line interval).

The traffic hindrance risk predicting unit 10 b predicts traffichindrance occurrence risk indicating possibility of traffic hindranceoccurring on the driving route based on traffic hindrance information,more exactly traffic information and weather information, as shown inFIG. 4.

Weather condition risks are classified into broad categories of “strandrisk,” “traffic hindrance risk” and “possibility of traffic hindrancerisk,” with weather condition hindrance degree (severity) being deemedto decrease in this order. “Strand risk” refers especially to likelihoodof the vehicle(s) 16 driving alone in sparse traffic having an accident,experiencing a disaster, getting stuck, or similar

“Traffic hindrance risk” and “possibility of traffic hindrance risk”refer to danger of, for example, getting caught in extremely sluggishtraffic or among many stranded vehicles under severe weather conditions(increased weather condition hindrance degree). In other words, traffichindrance risk and the like mean danger attributable to both weatherconditions and traffic hindrance. Between “Traffic hindrance risk” and“possibility of traffic hindrance risk,” the former is defined asrelating to higher degree of hindrance.

As shown in the drawings, weather conditions are of three types, snow,visibility and rain, whose risks are deemed to increase with increasingsnowfall per unit time, increasing rainfall per unit time and decreasingvisibility. Hindrance degree judged from foul weather information orfoul weather information and traffic hindrance in combination is sortedinto multiple level categories, C, B and A, by risk (rank value) inaccordance with hindrance degree. Rank value is defined to indicateincreasing hindrance degree in A, B and C order. In addition to theaforesaid, foul weather information also includes dense fog, strong windand all other phenomena constituting traffic hindrances.

FIG. 5 is an explanatory diagram showing “strand risk,” “traffichindrance risk” and “possibility of traffic hindrance risk” of FIG. 4 ingreater detail. In FIG. 5, instead of indicating risk using C, B and A,uppercase Roman numeral rank values I to III are used to indicate strandrisk, and lowercase Roman numeral rank values i to iv are used toindicate traffic hindrance risk and the like.

As shown in FIG. 5, “strand risk,” “traffic hindrance risk” and“possibility of traffic hindrance risk” are colored in colors defined inaccordance with rank value. Specifically, risk values I to III (or A)are colored red similarly to the numerical values 1 to 3 of congestiondegree information, risk values iii to iv (or B) are colored orangesimilarly to the numerical values 4 to 6 of congestion degreeinformation, and risk values i to ii (or C) are colored green similarlyto the numerical values 7 to 9 of congestion degree information.

As shown in the same drawing, similarly to the case of congestion degreeinformation, the rank value coloring is defined to vary in shade so asto become darker as degree of traffic hindrance increases. Specifically,foul weather period hindrance risk and possibility thereof are defined,as indicated by a straight arrow, to increase with larger numericalvalue as degree of weatherwise hindrance increases with foul weatherstrand risk, as indicated by a slant arrow.

In addition, as shown in FIG. 6, when the weather information includesat least one item of foul weather information among snow, rain andvisibility (snow in the illustrated example), the traffic hindrance riskindicating unit calculate and indicates a traffic hindrance occurrencerisk predictive value indicating possibility of traffic hindranceoccurring during a predetermined future period based on multiplecategories (ranks 0 to 3) obtained by analyzing foul weather informationand congestion degree information.

FIG. 6 shows an arrangement in which period up to 30 min from presenttime is considered current period, and traffic hindrance occurrence risk(more exactly, foul weather period traffic hindrance occurrence risk) upto 3 hr following current period (predetermined future period) ispredicted every 10 min.

In FIG. 6, as also touched on above, degree of hindrance judged fromcongestion degree information and strand risk, traffic hindrance risk,and possibility of traffic hindrance risk is indicated by rank values ofranks 0 to 3. Rank 0 corresponds to case of no actual hindrance expectedby 4 to 9 of congestion degree information or part of risk C withrespect to hindrance degree, and ranks 1 to 3 correspond to risks C to A(or I to III or i to iv).

FIG. 7 is a flowchart showing processing (operations) of the traffichindrance risk predicting unit 10 b.

Now to explain, in S200 a first link among links for which hindranceinformation is to be generated is retrieved. Specifically, the firstlink among links constituting predetermined driving sections (e.g., 10km sections) is selected, whereafter the program goes to S202, in whichit is determined whether congestion degree information has beencalculated.

When the result in S202 is NO, the program goes to S204 to select thenext link, and when YES, goes to S206, in which it is determined whetherthe weather information includes foul weather information containing anyof snow, rain or visibility. When the result in S206 is NO, the programgoes to S204, and when YES, goes to S208.

In the example shown in FIG. 6, the result of the determination of S206is YES because snow information is present. Needless to say, the samewould also apply in this case should foul weather information like thatshown in FIG. 4 include all of snow, rain and visibility.

In S208, it is determined whether a future predictive value estimatedfrom the weather model (value at time 10:40 or later of FIG. 6) ispresent, and when the result is NO, the program goes to S210, in whichthe current traffic hindrance rank is retrieved, namely, the currenttraffic hindrance rank at time of executing the flowchart of FIG. 7 isretrieved, whereafter the program goes to S212, in which (current)traffic hindrance risk is calculated or predicted (obtain calculationvalue) from the retrieved traffic hindrance rank.

On the other hand, when the result in S208 is YES, the program goes toS214, in which the current traffic hindrance rank is retrieved, namely,the final current traffic hindrance rank is retrieved, whereafter, theprogram goes to S216, in which a future traffic hindrance riskpredictive value, namely one at time of executing the flowchart of FIG.7, is calculated from the retrieved final current hindrance rank andfoul weather information (snow information) corresponding to thedetected traffic hindrance risk and time of executing the flowchart ofFIG. 7, and the calculated predictive value is indicated.

Now follows an explanation of the processing (operations) of the meshsurface conversion processing unit 10 e and the traffic hindrance riskindication data generating unit 10 f of the apparatus 10 of FIG. 1.

FIG. 8 is a flowchart showing this processing, and FIGS. 9 to 12 areexplanatory diagram explaining the processing of the FIG. 8 flowchart.

Now to explain with reference to FIG. 8, first, in S300, traffichindrance risks calculated in link units are drawn on a mesh map. Morespecifically, as shown in FIG. 9, links constituting the aforesaidpredetermined driving sections are drawn on the mesh map all at one time(all links are not shown in FIG. 9).

Next, in S302, it is determined whether a numerical value (indicatingcongestion degree information) exists for individual links of apredetermined area (area region including aforesaid predetermineddriving sections).

These values are shown in FIG. 10. Although they are the same as thoseindicating congestion degree information shown in FIG. 4, in thisexample, 4, 7 and 8 are treated to be not colored because risk oftraffic hindrance occurrence is considered low under the currentcircumstances, i.e., they are deemed categories whose congestion degreesare at or below a predetermined degree.

Therefore, the result in S302 is NO regarding links having numericalvalues 4, 7 and 8, so, as shown in FIG. 11, the program goes to S304, inwhich the links corresponding to these are deleted and the remaininglinks are colored in colors corresponding to values 1, 2, 3, 5, 6, 8 and9.

On the other hand, when the result in S302 is YES, the program goes toS306, in which the link of lowest risk, i.e., the link with largestnumerical value, in FIG. 10, for instance, the link corresponding tonumerical value 9, is selected, whereafter the program goes to S308, inwhich the mesh containing that link is selected, and to S310, in whichthe mesh is colored in a color corresponding to the numerical value.

Specifically, as shown in FIGS. 12 and 13, a region (square region)having as its summit a surface including the start point and end pointof the link concerned is colored in a color corresponding to thenumerical value.

Next, in S312, it is determined whether an identical numerical valueexists, and when the result is YES, the program goes to S314 to selectanother link and then returns to S308. When the result in S312 is NO,the program goes to S316, in which it is determined whether a lowernumerical value (6 in FIG. 10, for instance) exists, and when the resultis YES, goes to S318 to retrieve that link.

The program next returns to S308 and repeats the aforesaid processing.When the result is NO in S316, further processing is terminated.

To explain in further detail, as seen in FIG. 13, when congestion degreeinformation drawn with respect to the same link is overlaid by repeatingthe aforesaid processing, colors corresponding to numerical values ofmore severe congestion degrees posing greater traffic hindrance risk arewritten over colors corresponding to numerical values of mildercongestion degrees posing slighter risk of traffic hindrance, wherebypinpoint indication of congestion degree becomes possible and enhancedviewability is obtained.

Thus, in accordance with the flowchart of FIG. 7, the traffic hindrancerisk predicting unit 10 b in the apparatus 10 predicts risk of trafficcongestion occurrence (calculates predictive values) based on weatherdata, tubular data shown in FIGS. 4 to 6, tubular data generated by theprocessing of FIG. 8 and mesh format data shown in FIG. 13.

Further, the processing of FIG. 8 indicates the generated indicationdata shown in FIG. 13 on the display 10 g so as to be viewable by theoperator, whereby the operator is offered enhanced data visibility. Asthe operator can therefore concomitantly view traffic hindrance riskpredictive values calculated by the processing of FIG. 7, the operatorcan accurately determine traffic hindrance degree.

There now follows an explanation of other processing (operations) of themesh surface conversion processing unit 10 e and the traffic hindrancerisk indication data generating unit 10 f of the apparatus 10 of FIG. 1,namely, of mesh surface conversion processing of indication data shownin FIGS. 4 and 5.

FIG. 14 is a flowchart showing this processing.

Now to explain with reference to this drawing, first, in S400,processing similar to that of S300 of FIG. 8 is performed to draw alllinks constituting predetermined driving sections on a mesh map at onetime.

Next, in S402, the link of lowest risk, which in the example shown inFIG. 5 would be the link corresponding to Roman numeral i (rank value),for instance, is selected, whereafter the program goes to S404, in whichthe mesh containing that link is selected, and to S406, in which themesh is colored in a color corresponding to the rank value. In theprocessing of FIG. 14, color data are generated only for “possibility offoul weather period hindrance risk” and “foul weather period hindrancerisk” on right side of FIG. 5.

Next, in S408, it is determined whether an identical rank value exists,and the program returns to S402 when the result is YES. When the resultin S408 is NO, the program goes to S410 to determine whether a higherrank value (ii in FIG. 5, for instance) exists. When the result in S410is YES, the program returns to S402 to repeat the aforesaid processing,and when NO, terminates further processing.

When foul weather information and congestion degree information withrespect to the same link are overlaid by repeating the aforesaidprocessing, similar to the processing at the FIG. 8 flowchart, colorscorresponding to rank values of large traffic hindrance risk areoverwritten by colors corresponding to larger rank values, wherebypinpoint indication of foul weather period congestion degree orpossibility thereof becomes possible.

Tabular data of FIGS. 4 to 6 generated by the processing of FIG. 14 areindicated or displayed on the display 10 g to be viewable by theoperator. Therefore, by concomitantly viewing the traffic hindrance riskpredictive values calculated by the processing of the FIG. 7 flowchart,the operator can accurately determine traffic hindrance degree.

As set forth above, the embodiment is configured to have an apparatus(10) or method for predicting a traffic hindrance risk, comprising: atraffic hindrance information generating server (12, S10-S14, S100-S106)configured to generate traffic hindrance information encompassingcongestion degree information including a current congestion degreeobtained from a current value of driving data transmitted as probe data(20) regarding a driving route from a vehicle(s) (16) equipped with anavigation system (16 a) and a statistical congestion degree obtainedfrom a statistical value of the current value of driving data in acertain previous period; a weather information generating server (14)configured to generate weather information from weather data (includingweather observation values, weather analysis values and weather forecastvalues) distributed by a meteorological agency (14 a) with respect to anarea including the driving route based on an weather model (14 d); and atraffic hindrance risk predicting unit (10 b, S200-S216) configured topredict a traffic hindrance occurrence risk meaning possibility oftraffic hindrance occurrence with respect to the driving route based onthe congestion degree information generated by the traffic hindranceinformation generating server and the weather information generated bythe weather information generating server, alternatively, it has atleast one processor and a memory coupled to the processor; wherein theprocessor and the memory are configured to perform (or the methodcomprises the steps of): predicting a traffic hindrance occurrence riskmeaning possibility of traffic hindrance occurrence with respect to thedriving route based on the congestion degree information generated bythe traffic hindrance information generating server and the weatherinformation generated by the weather information generating server.

With this, the traffic hindrance occurrence risk can be definitelypredicted based on the traffic hindrance information meaning congestiondegree acquired from driving data transmitted from vehicle(s) 16 andweather information.

In the apparatus or method, the traffic hindrance information generatingserver (12) is configured to generate the congestion degree informationby sorting it into a plurality of level categories (one among one tonine) in accordance with a driving speed in the driving data.

With this, the traffic hindrance occurrence risk can be more definitelypredicted.

In the apparatus or method, the traffic hindrance risk predicting unit(or the step of predicting) is configured to predict foul weathertraffic hindrance occurrence risk meaning possibility of traffichindrance occurrence under foul weather, when the weather informationincludes at least one item of foul weather information among snow, rainand visibility, based on a plurality of categories (risk A to C, rankvalues i to iv) acquired by analyzing foul weather information (or theprocessor and the memory are configured to perform the predicting suchthat foul weather traffic hindrance occurrence risk meaning possibilityof traffic hindrance occurrence under foul weather is predicted, whenthe weather information includes at least one item of foul weatherinformation among snow, rain and visibility, based on a plurality ofcategories acquired by analyzing the foul weather information).

With this, the foul weather traffic hindrance occurrence risk can bedefinitely predicted.

In the apparatus or method, the traffic hindrance information generatingserver is configured to generate the congestion degree information forindividual links that define the driving route, the weather informationgenerating server is configured to generate the weather information withrespect to individual areas including the driving route, and the traffichindrance risk predicting unit (or the step of predicting) is configuredto predict at least one of the traffic hindrance occurrence risk and thefoul weather traffic hindrance risk based on the weather informationgenerated for the individual areas and the congestion degree informationgenerated for the individual links (or the processor and the memory areconfigured to perform the predicting such that at least one of thetraffic hindrance occurrence risk and the foul weather traffic hindrancerisk is predicted based on the weather information generated for theindividual areas and the congestion degree information generated for theindividual links) (S200-S216).

With this, the traffic hindrance occurrence risk can be more definitelypredicted by predicting it based on the weather information generatedfor the individual areas and the congestion degree information generatedfor the individual links.

In the apparatus and method, the traffic hindrance informationgenerating server is configured to generate the congestion degreeinformation for individual predetermined driving sections (e.g., 10 kmsquare).

With this, in addition to the aforesaid effects and advantages, datarequired for, inter alia, calculating and displaying predictive valuescan be reliably generated.

It should be noted that, although this specification states that thetraffic hindrance information, including congestion degree informationcomposed of current congestion degrees and statistical congestiondegrees obtained from statistical values, is generated by the hindranceinformation generating server 12, such information can alternatively begenerated by the apparatus 10.

While the present invention has been described with reference to thepreferred embodiments thereof, it will be understood, by those skilledin the art, that various changes and modifications may be made theretowithout departing from the scope of the appended claims.

What is claimed is:
 1. An apparatus for predicting a traffic hindrancerisk, comprising: a traffic hindrance information generating serverconfigured to generate traffic hindrance information encompassingcongestion degree information including a current congestion degreeobtained from a current value of driving data transmitted as probe dataregarding a driving route from a vehicle equipped with a navigationsystem and a statistical congestion degree obtained from a statisticalvalue of the current value of driving data in a certain previous period;a weather information generating server configured to generate weatherinformation from weather data distributed by a meteorological agencywith respect to an area including the driving route based on an weathermodel; and a traffic hindrance risk predicting unit configured topredict a traffic hindrance occurrence risk possibility of traffichindrance occurrence with respect to the driving route based on thecongestion degree information generated by the traffic hindranceinformation generating server and the weather information generated bythe weather information generating server.
 2. The apparatus according toclaim 1, wherein the traffic hindrance information generating server isconfigured to generate the congestion degree information by sorting itinto a plurality of level categories in accordance with a driving speedin the driving data.
 3. The apparatus according to claim 1, wherein thetraffic hindrance risk predicting unit is configured to predict foulweather traffic hindrance occurrence risk meaning possibility of traffichindrance occurrence under foul weather, when the weather informationincludes at least one item of foul weather information among snow, rainand visibility, based on a plurality of categories acquired by analyzingfoul weather information.
 4. The apparatus according to claim 3, whereinthe traffic hindrance information generating server is configured togenerate the congestion degree information for individual links thatdefine the driving route, the weather information generating server isconfigured to generate the weather information with respect toindividual areas including the driving route, and the traffic hindrancerisk predicting unit is configured to predict at least one of thetraffic hindrance occurrence risk and the foul weather traffic hindrancerisk based on the weather information generated for the individual areasand the congestion degree information generated for the individuallinks,
 5. The apparatus according to claim 1, wherein the traffichindrance information generating server is configured to generate thecongestion degree information for individual predetermined drivingsections.
 6. An apparatus for predicting a traffic hindrance risk,comprising: a traffic hindrance information generating server configuredto generate traffic hindrance information encompassing congestion degreeinformation including a current congestion degree obtained from acurrent value of driving data transmitted as probe data regarding adriving route from a vehicle equipped with a navigation system and astatistical congestion degree obtained from a statistical value of thecurrent value of driving data in a certain previous period; a weatherinformation generating server configured to generate weather informationfrom weather data distributed by a meteorological agency with respect toan area including the driving route based on an weather model; and atleast one processor and a memory coupled to the processor; wherein theprocessor and the memory are configured to perform; predicting a traffichindrance occurrence risk possibility of traffic hindrance occurrencewith respect to the driving route based on the congestion degreeinformation generated by the traffic hindrance information generatingserver and the weather information generated by the weather informationgenerating server.
 7. The apparatus according to claim 6, wherein thetraffic hindrance information generating server is configured togenerate the congestion degree information by sorting it into aplurality of level categories in accordance with a driving speed in thedriving data.
 8. The apparatus according to claim 6, wherein theprocessor and the memory are configured to perform the predicting suchthat foul weather traffic hindrance occurrence risk meaning possibilityof traffic hindrance occurrence under foul weather is predicted, whenthe weather information includes at least one item of foul weatherinformation among snow, rain and visibility, based on a plurality ofcategories acquired by analyzing the foul weather information.
 9. Theapparatus according to claim 8, wherein the traffic hindranceinformation generating server is configured to generate the congestiondegree information for individual links that define the driving route,the weather information generating server is configured to generate theweather information with respect to individual areas including thedriving route, and the processor and the memory are configured toperform the predicting such that at least one of the traffic hindranceoccurrence risk and the foul weather traffic hindrance risk is predictedbased on the weather information generated for the individual areas andthe congestion degree information generated for the individual links.10. The apparatus according to claim 6, wherein the traffic hindranceinformation generating server is configured to generate the congestiondegree information for individual predetermined driving sections.
 11. Amethod for predicting a traffic hindrance risk, having: a traffichindrance information generating server configured to generate traffichindrance information encompassing congestion degree informationincluding a current congestion degree obtained from a current value ofdriving data transmitted as probe data regarding a driving route from avehicle equipped with a navigation system and a statistical congestiondegree obtained from a statistical value of the current value of drivingdata in a certain previous period; and a weather information generatingserver configured to generate weather information from weather datadistributed by a meteorological agency with respect to an area includingthe driving route based on an weather model; wherein the methodcomprising the step of; predicting a traffic hindrance occurrence riskpossibility of traffic hindrance occurrence with respect to the drivingroute based on the congestion degree information generated by thetraffic hindrance information generating server and the weatherinformation generated by the weather information generating server. 12.The method according to claim 11, wherein the traffic hindranceinformation generating server is configured to generate the congestiondegree information by sorting it into a plurality of level categories inaccordance with a driving speed in the driving data.
 13. The methodaccording to claim 11, wherein the step of predicting is configured topredicts the foul weather traffic hindrance occurrence risk meaningpossibility of traffic hindrance occurrence under foul weather, when theweather information includes at least one item of the foul weatherinformation among snow, rain and visibility, based on a plurality ofcategories acquired by analyzing foul weather information.
 14. Themethod according to claim 13, wherein the traffic hindrance informationgenerating server is configured to generate the congestion degreeinformation for individual links that define the driving route, theweather information generating server is configured to generate theweather information with respect to individual areas including thedriving route, and the step of predicting is configured to predict atleast one of the traffic hindrance occurrence risk and the foul weathertraffic hindrance risk based on the weather information generated forthe individual areas and the congestion degree information generated forthe individual links,
 15. The method according to claim 11, wherein thetraffic hindrance information generating server is configured togenerate the congestion degree information for individual predetermineddriving sections.